KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics

TL;DR

KarmaTS is a versatile simulation platform that generates synthetic multivariate time series data with known causal structures, aiding causal discovery validation and benchmarking.

Contribution

It introduces a novel interactive framework combining expert knowledge and algorithms to construct complex causal models for multivariate time series simulation.

Findings

Supports simulation with known causal dynamics

Handles mixed variable types and causal edges

Enables validation of causal discovery algorithms

Abstract

We introduce KarmaTS, an interactive framework for constructing lag-indexed, executable spatiotemporal causal graphical models for multivariate time series (MTS) simulation. Motivated by the challenge of access-restricted physiological data, KarmaTS generates synthetic MTS with known causal dynamics and augments real-world datasets with expert knowledge. The system constructs a discrete-time structural causal process (DSCP) by combining expert knowledge and algorithmic proposals in a mixed-initiative, human-in-the-loop workflow. The resulting DSCP supports simulation and causal interventions, including those under user-specified distribution shifts. KarmaTS handles mixed variable types, contemporaneous and lagged edges, and modular edge functionals ranging from parameterizable templates to neural network models. Together, these features enable flexible validation and benchmarking of causal discovery algorithms through expert-informed simulation.